Controlling hydrocarbon production

ABSTRACT

Techniques for controlling hydrocarbon production includes (i) identifying a plurality of reservoir measurements of a subterranean hydrocarbon reservoir located between at least one injection wellbore and at least one production wellbore; (ii) processing the identified plurality of reservoir measurements to generate a petrophysical model of the subterranean hydrocarbon reservoir; (iii) determining, based on the petrophysical model, a flow of an injectant from the injection wellbore toward the production wellbore; and (iv) adjusting an inflow control device (ICD) positioned about the production wellbore based on the determined flow of the injectant.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of and claims the benefit of priorityunder 35 U.S.C. § 120 to U.S. patent application Ser. No. 15/690,600,filed on Aug. 30, 2017, which claims priority under 35 U.S.C. § 119 toU.S. Provisional Patent Application Ser. No. 62/383,240, filed on Sep.2, 2016, the entire contents of which are incorporated by referenceherein.

TECHNICAL FIELD

This document relates controlling hydrocarbon production and, moreparticularly, controlling hydrocarbon production with one or more inflowcontrol devices.

BACKGROUND

Inflow control devices (ICD) and inflow control valves (ICV) may be usedin hydrocarbon production wells to control the production of oil, gas,or both, along the well completion. They can be used to isolate specificwell segments while allowing other segments to continue to contribute toproduction. Such devices may be useful when an injectant breakthroughoccurs, for example, due to high permeability streaks which can be aresult of areal and vertical heterogeneity of a reservoir. The ICDs,ICVs, or both, can be used to isolate the well segment that is incontact with a high conductivity, early injectant arrival path throughthe reservoir and maintain production from the other well segments thatdo not encounter this situation. Injectant breakthrough can occursuddenly, and there may be limited methods to detect it prior to itsoccurrence. Early injectant breakthrough can result in excessiveinjectant production and associated lifting and processing costs. It canalso lead to loss of valuable reservoir drive energy provided by naturalaquifer, gas-cap drive, or injectant and therefore may result in loweroil recovery compared to the true potential of a reservoir.

SUMMARY

In an example implementation, a computer-implemented method forcontrolling hydrocarbon production includes (i) identifying a pluralityof reservoir measurements of a subterranean hydrocarbon reservoirlocated between at least one injection wellbore and at least oneproduction wellbore; (ii) processing the identified plurality ofreservoir measurements to generate a petrophysical model of thesubterranean hydrocarbon reservoir; (iii) determining, based on thepetrophysical model, a flow of an injectant from the injection wellboretoward the production wellbore; and (iv) adjusting an inflow controldevice (ICD) positioned about the production wellbore based on thedetermined flow of the injectant.

An aspect combinable with the general implementation further includesreceiving the plurality of reservoir measurements from one or moresensors positioned at a terranean surface or in the reservoir.

In another aspect combinable with any of the previous aspects, the oneor more sensors are positioned in the reservoir between the injectionwellbore and the production wellbore.

In another aspect combinable with any of the previous aspects, thereservoir measurements include at least one of crosswell electromagnetic(EM), borehole EM, surface electromagnetics, gravity measurements, or 4Dseismic.

In another aspect combinable with any of the previous aspects, at leastone of the injection wellbore or the production wellbore includes ahorizontal wellbore.

In another aspect combinable with any of the previous aspects,processing the identified plurality of reservoir measurements includesinverting the reservoir measurements to determine the petrophysicalmodel.

In another aspect combinable with any of the previous aspects, thepetrophysical model includes a water saturation value at a plurality oflocations in the reservoir between the injection wellbore and theproduction wellbore.

In another aspect combinable with any of the previous aspects, invertingthe reservoir measurements includes executing the Archie algorithm tothe reservoir measurements.

In another aspect combinable with any of the previous aspects,determining the injectant flow includes determining a floodfront betweenthe injection wellbore and the production wellbore.

In another aspect combinable with any of the previous aspects, thefloodfront includes a sum of the water saturation and a hydrocarbonsaturation value at the plurality of locations.

In another aspect combinable with any of the previous aspects,determining the injectant flow includes updating the petrophysicalmodel.

In another aspect combinable with any of the previous aspects, updatingthe petrophysical model includes using a Bayesian inference with theplurality of reservoir measurements.

Another aspect combinable with any of the previous aspects furtherincludes determining a threshold location between the injection wellboreand the production wellbore.

In another aspect combinable with any of the previous aspects,determining the flow of the injectant includes determining the flow ofthe injectant at the threshold location.

In another aspect combinable with any of the previous aspects, adjustingthe ICD includes adjusting the ICD based on the flow of the injectant atthe threshold location exceeding a predetermined value.

In another aspect combinable with any of the previous aspects, adjustingthe ICD includes shutting the ICD.

Another aspect combinable with any of the previous aspects furtherincludes executing an iterative process of steps (i) through (iv).

In another aspect combinable with any of the previous aspects, theiterative process includes comparing a previous plurality of reservoirmeasurements with a current plurality of reservoir measurements.

Another aspect combinable with any of the previous aspects furtherincludes stopping the iterative process when a difference between thecurrent plurality of reservoir measurements and the previous pluralityof reservoir measurements is less than a threshold value.

One, some, or all of the implementations according to the presentdisclosure may include one or more of the following features. Forexample, implementations of an injectant flood detection system thatincorporates, for instance, deep reservoir measurements (for example,crosswell electromagnetic (EM), borehole, surface electromagnetics,gravity measurements, 4D seismic, or a combination thereof) may detect amovement of secondary/tertiary flood front towards a production wellearlier than conventional techniques. Implementations of an injectantflood detection system may respond to an approaching injectant floodfront by controlling or adjusting ICDs or ICVs that can be used tomitigate early injectant breakthrough by throttling, restricting, orisolating the well-segments that will most likely encounter abreakthrough prior to the flood event. Implementations of an injectantflood detection system may activate selected ICDs to slow down amovement of arriving injectant-front and divert it to an unswept part ofthe reservoir. Implementations of an injectant flood detection systemmay execute a dynamic operation of the ICDs based on advanced detectionof the secondary/tertiary flood front for optimizing oil production. Insome aspects, an injectant flood detection system according to thepresent disclosure may provide for an enhanced sweep efficiency andincreased oil recovery relative to conventional flood detectiontechniques. Implementations of an injectant flood detection system mayalso provide for an early detection of injectant front movement in areservoir away from an injection well, optimal operation of ICDs andICVs, prolonging of well life, and a reduction in produced injectanthandling costs.

Implementations of the embodiments described in the present disclosuremay include systems and computer-readable media. For example, a systemof one or more computers can be configured to perform particular actionsby virtue of having software, firmware, hardware, or a combination ofthem installed on the system that in operation causes or cause thesystem to perform the actions. One or more computer programs can beconfigured to perform particular actions by virtue of includinginstructions that, when executed by data processing apparatus, cause theapparatus to perform the actions.

Table 1 includes nomenclature and abbreviations that may be used in thepresent disclosure:

TABLE 1 Abbreviation Term and Units B_(o) oil formation volume factor inreservoir barrel per stock tank barrel (rb/stb) BHP_(max) maximumbottomhole pressure constraint in pounds per square inch (psi) BHP_(min)minimum bottomhole pressure constraint in pounds per square inch (psi) kpermeability in milli-Darcies(mD) L_(d) dimensionless distance betweeninjection and production wells (L_(d))_(opt) dimensionless optimumlocation between injection and production wells P_(bp) bubble pointpressure in pounds per square inch (psi) P_(i) initial reservoirpressure in pounds per square inch (psi) q_(o) constant oil productionrate constraint in stock tank barrels per day(stb/d) q_(w) constantwater injection rate constraint in stock tank barrels per day (stb/d)S_(w) water saturation in fraction S_(wi) initial water saturation infraction

The details of one or more embodiments are set forth in the accompanyingdrawings and the description. Other features, objects, and advantageswill be apparent from the description and drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1A illustrates a schematic diagram showing an injection wellboreand a production wellbore.

FIG. 1B illustrates a schematic diagram showing the injection wellboreand the production wellbore and multiple ICDs/ICVs positioned on theproduction wellbore, along with flood front detection locations betweenthe injection and production wellbores.

FIG. 1C illustrates an example method for controlling hydrocarbonproduction.

FIG. 2 illustrates several synthetic geomodels with differentheterogeneity in a permeability field in a simulation model.

FIGS. 3A-3B illustrate water saturation maps for heterogeneous andhomogeneous cases that do not include ICDs or ICVs in a simulationmodel.

FIGS. 4A-4B illustrate cumulative oil and water production graphs overtime in heterogeneous and homogeneous cases that do not include ICDs orICVs in a simulation model.

FIGS. 5A-5B illustrate water saturation maps for heterogeneous andhomogeneous cases that include ICDs or ICVs in a simulation model.

FIGS. 6A-6B illustrate cumulative oil and water production graphs overtime in cases that include ICDs or ICVs with early detection ofinjectant flood front in a simulation model.

FIG. 7 illustrates effects of operation of ICDs or ICVs on a productionwellbore due to early detection of an injectant flood front in asimulation model.

FIG. 8 illustrates optimum locations for early injectant-front detectionfor several different geomodels in a simulation model.

FIG. 9 depicts a schematic diagram of a control system that may beapplied to any of the computer-implemented methods and other techniquesdescribed in the present disclosure.

FIG. 10 illustrates a chart that shows location and movement of aflood-front through a mapping of first and or second derivatives (ratesof changes).

DETAILED DESCRIPTION

This document discusses systems, methods, and computer-readable mediafor controlling hydrocarbon production from one or more productionwellbores through control of one or more ICDs or ICVs that arepositioned on the production wellbore based on detection of movement ofa secondary or tertiary injection flood from one or more injectionwellbores. For example, in some aspects, implementations of an injectantdetection system and workflow described in the present disclosure mayutilize deep reservoir measurement in managing and optimizing asecondary/tertiary flood of a reservoir in a systematic and robustapproach. Further, the injectant detection system and workflow maydetermine (all or partially) reservoir transmissibility in a volumebetween the injection and production wells. The injectant detectionsystem and workflow may also actively optimize production and ultimatelyincrease the recovery factor from a reservoir.

FIG. 1A illustrates a schematic diagram showing a system 100 thatincludes an injection wellbore 102 (“injector”) and a productionwellbore 104 (“producer”). FIG. 1A illustrates a schematic plan viewshowing the wellbores 102 and 104 as single, horizontal wellboresseparated by, in this example, 10,000 feet. In this example, eachwellbore 102 and 104 extends horizontally through a hydrocarbon bearingformation, or reservoir, for 42,000 feet. Other example dimensions ofthe wellbores 102 and 104, as well as their distance apart, arecontemplated by the present disclosure.

Wellbores 102 and 104 are illustrated as single leg, horizontalwellbores. Other types of wellbores are also contemplated by the presentdisclosure. For example, one or both of the injection wellbore 102 andthe production wellbore 104 may be vertical wellbores. One or both ofthe injection wellbore 102 and the production wellbore 104 may havemultiple laterals that extend from the respective wellbore. One or bothof the injection wellbore 102 and the production wellbore 104 may becased or uncased. In some aspects, although FIG. 1A illustrates thewellbores 102 and 104 as being of the same or similar vertical depth(for example, within a common horizontal plane in the reservoir), thewellbores 102 and 104 may be formed within the reservoir at differentvertical depths. In some cases, there may be multiple wellbores 102 anda single wellbore 104, multiple wellbores 104 and a single wellbore 102,or multiple wellbores 102 and multiple wellbores 104 in a particularreservoir.

Generally, the injection wellbore 102 is used to inject a fluidtherethrough and into the reservoir in a secondary or tertiary recoveryprocess. For example, a fluid, such as water or gas, may be pumpedthrough the wellbore 102 into the reservoir to maintain reservoirpressure so that hydrocarbons may be produced from the productionwellbore 104. In some instances, for example, separated gas from theproduction wellbore 104 (or other producers) may be reinjected into thereservoir from the wellbore 102. In some cases, such as offshore wells,water may be injected into the reservoir from the wellbore 102.

Sweep efficiency, for example, is a measure of the effectiveness of aninjection process to help boost hydrocarbon recovery. Sweep efficiency,for example, depends on a volume of the reservoir contacted by theinjected fluid, and may depend on many factors, such as an injectionpattern, off-pattern wells, fractures in the formation, reservoirthickness, permeability and areal and vertical heterogeneity, mobilityratio, density difference between the injectant (the injected fluid) andthe displaced fluid (the hydrocarbons to be produced, and flow rates).

FIG. 1B illustrates a schematic diagram showing the injection wellbore102 and the production wellbore 104 and multiple ICDs/ICVs 106positioned on the production wellbore, along with flood front detectionlocations between the injection and production wellbores 102 and 104.Generally, an ICD is a component installed as part of a well completionto help optimize production by equalizing reservoir inflow along alength of the wellbore. In some instances, as is shown in this figure,multiple ICDs can be installed along the reservoir section of thewellbore 104. Each ICD 106 can be configured to a specific setting topartially choke flow of hydrocarbons into the production wellbore 104.Thus, the ICDs 106 can be used to delay or direct hydrocarbons intoparticular sections of the production wellbore 104 by reducing anannular velocity across a selected interval of the wellbore 104.

Generally, an ICV is an active component (whereas, in some aspects, anICD is a passive component) that can be controlled from a terraneansurface to maintain flow conformance into the production wellbore 104.The ICV can also be controlled to stop unwanted fluids (for example,injectant fluid) from entering the wellbore 104. In some aspects, an ICVcan be connected, for control purposes, to a cable that extends to thesurface that provides electric conduits, hydraulic conduits, or both torelay commands from the surface to the ICV. Alternatively, oradditionally, the ICV can be controlled from a downhole controller thatis located, for example, in the injection wellbore 102 or productionwellbore 104. In the present disclosure, the terms ICD and ICV aregenerally interchangeable, as both may refer to a flow control apparatusthat can be controlled from the surface.

FIG. 1B illustrates the injection wellbore 102 and production wellbore104 in a simulation of an injectant detection system and processaccording to the present disclosure. The injectant detection system andprocess can also control the ICDs 106 to control hydrocarbon productioninto the production wellbore 104 from the reservoir (for example,control flow rate and inflow location). Injection and productionconstraints are illustrated in FIG. 1B for the example simulation, whichsimulates a water flood injection from the wellbore 102 moving throughthe reservoir toward the wellbore 104.

Deep reservoir measurements can be taken at the surface or within thereservoir, itself. For example, in some aspects, deep reservoirmeasurements such as seismic, electromagnetic, and gravity measurements,may be taken by surface-located sensors. In alternative aspects, or inaddition to such surface measurements, deep reservoir measurements maybe taken by sensors in a subterranean zone of the reservoir, such ascrosswell EM or borehole to surface EM sensors placed in one or both ofthe wellbores 102 and 104. Also, the dotted lines running parallel tothe wellbores 102 and 104 illustrate example locations between thesewellbores at which deep reservoir measurements may be taken within thereservoir (for example, in additional directional or verticalwellbores). The deep reservoir measurements may include, for example,crosswell EM, borehole, surface electromagnetics, gravity measurements,or 4D seismic surveys, or a combination thereof. As explained in moredetail in the present disclosure, such measurements may be used to moreaccurately predict, for example, a waterflood, as well as predict suchan event earlier.

Crosswell electromagnetic (EM), generally, involves applying inductivephysics and vertical well 2D inversion to interrogate an inter-wellresistivity distribution. Crosswell EM measures a physical parameter,such as the vertical magnetic component of the electromagnetic fieldtransmitted through the reservoir. Crosswell EM may be applied to bothpairs of vertical wells and pairs of horizontal wells. When the wellsare oriented horizontally, sensors may be sensitive to both vertical andhorizontal variations, transposing the inversion into a 3D problem. TheEM data can be assembled into a digital geologic model to thenconstructs a 3D resistivity volume prior to inversion (described later).

The simulation depicted in FIG. 2, and other subsequent figures, modelsa synthetic horizontal well pair (in other words, the wellbores 102 and104) model built using a black-oil reservoir simulator to studyperformance of waterflooding in a single layer heterogeneous oilreservoir to demonstrate the injectant detection system and processesdescribed in the present disclosure, which includes using informationobtained from deep reading technology to control ICV/ICDs for optimizingwaterfloods. The simulation results show that using ICV/ICDs at thehorizontal production well 104 according to the injectant detectionsystem and processes may significantly improve sweep efficiency andreduce water production. Further, early detection of water front withdeep reading technologies provides incremental oil recovery. Thesimulation also shows that an optimum location for water-front detectionmay exist between the injector 102 and the producer 104 to improve oilproduction with specified injection and production constraints (forexample, shown in FIG. 1B). Deep reading technologies may also providevaluable information about the mobility field between the well-pair toreduce uncertainty in heterogeneity, which can be used to update thegeological model for better history matching and forecasting production.

Conventionally, ICVs or ICDs may react after the breakthrough of aninjectant at a production well. However, based on the simulation, in theinjectant detection system and processes of the present disclosure,control of ICVs or ICDs 106 based on early front detection improvessweep efficiency and reduces water-to-oil ratio in horizontalwaterfloods.

As noted, a single-layer simulation model (140×80 grids) with twohorizontal wells (wellbores 102 and 104, as shown in FIG. 1B) is builtto simulate waterflooding using any available black-oil reservoirsimulator (for example, CMG-IMEX from Computer Modeling Group ofCalgary, Alberta, Canada, or any other available reservoir simulator).The length of both wellbores 102 and 104 and the well spacing are 30,000ft. and 10,000 ft., respectively. Since this is a single layer model,the gravity segregation is ignored and areal sweep efficiency is used asan indicator of increased recovery. Initial reservoir pressure is set to4400 psi. Connate water saturation, which is equal to irreducible watersaturation, is 0.2. Water is injected at a constant rate of 5500 bbl/dayand at a maximum bottom-hole pressure of 5500 psi. Oil is produced at aconstant rate of 5000 bbl/day (Oil Formation Volume Factor=1.1 rb/stb),which ensures voidage replacement. Bubble point pressure is set to 2000psi. Minimum bottom-hole pressure assigned for the producer 104 is 2200psi to prevent free gas generation around the producer 104.Independently acting ICDs 106 are assigned for each perforation on thehorizontal producer 104. If water front is detected at the productionwell 104, a particular ICD 106 is shut in when watercut at thecorresponding perforation is larger than 10%. If water front is detectedat a distance from the production well 104, then a particular ICD 106 isshut in when water saturation in the assigned distant grid is largerthan 0.2.

FIG. 1C illustrates an example method 150 for controlling hydrocarbonproduction. Method 150 may be executed, at least in part, by a controlsystem or controller (for example, microprocessor based controller, PLC,electromechanical, electronic, pneumatic, or other form as appropriate)that is communicably coupled to control the ICDs and ICVs 106. Thecontrol system may include components as described with reference toFIG. 9.

Method 150 may begin at step 152, which includes identifying a pluralityof reservoir measurements of a subterranean hydrocarbon reservoirlocated between at least one injection wellbore (102) and at least oneproduction wellbore (104). For example as described, deep reservoirmeasurements may be taken from the surface or in the reservoir, such ascrosswell EM, gravity, seismic, 4D seismic, or a combination thereof. Insome aspects, the measurements may be taken prior to forming one or bothof wellbores 102 and 104 and stored for later identification (forexample, in a computer-readable database). In some aspects, suchmeasurements may be performed in real time, for example, as or shortlyafter injectant is circulated through the wellbore 102 into thereservoir.

Method 150 may continue at step 154, which includes processing theidentified plurality of reservoir measurements to generate apetrophysical model of the subterranean hydrocarbon reservoir. This stepmay include, for example, inversion of the plurality of reservoirmeasurements, such as inversion of the crosswell EM data. The inversionof such data may include, for example, obtaining a probable distributionof resistivity in the reservoir that is compatible with the measureddata set of magnetic field. Thus, the inversion may be an inference of aresistivity distribution (for example, in a 3D cube volume) that iscompatible with the measured data. Other processing techniques may alsobe applicable to other forms of measured deep reservoir data, such aspressure transients, temperature gradients, gravity, and other data.

The petrophysical model that results from the inversion can be aphysical parameter, such as water or hydrocarbon saturation, distributedin the reservoir at a particular time. For example, the Archie equation(Equation 1) may be used to convert the inverted crosswell EM data tothe petrophysical model:

$\begin{matrix}{{{Sw}^{n} = {\frac{a}{\phi^{m}}\left( \frac{R_{w}}{R_{t}} \right)}},} & {{Equation}\mspace{14mu} 1}\end{matrix}$

where S_(w)=water saturation, n=saturation exponent-2, ϕ=porosity, m=anexponent, R_(w)=resistivity of water in the pore space, andR_(t)=formation resistivity. The Archie equation may be used to convertthe deep reading resistivity (assumed to be R_(t)) to a watersaturation, S_(w). When S_(w) is mapped spatially, a position of thewater floodfront can be inferred. Other equations may be used as well.For example, an algorithm other than the Archie equation that cancompute water saturation from the reservoir measurements may be used instep 154. Further, in some examples, the reservoir may be comprised of arock formation that is incompatible with the Archie equations, such as ashale sand. In such formations, another model for computing watersaturations for those types of reservoirs may be used.

Resistivity at each grid-block (shown in FIG. 1A) interpreted fromcrosswell EM surveys can be converted to water saturation usingappropriate petrophysical relationships (such as Equation 1) andcompared to saturations predicted by reservoir simulations. This stepallows obtaining maps of fluid distribution (saturation) severalhundreds of meters from the wellbores 102 and 104, deep inside thereservoirs. In some aspects, method 150 may be repeated in an iterativeprocess, thus providing a view of changes in saturation and injectantfront location with time (time-lapse monitoring).

In some aspects, method 150 can generate many different realizations ofreservoir properties by using the same crosswell EM response. Inaddition to the crosswell EM response, integration of other deepreservoir data sources (for example, gravity measurement, seismicresponse, sonic and resistivity logs) with reservoir property modelingmay reduce uncertainty. For instance, reservoir parameters that are moresensitive (relatively) to the crosswell EM response may be identifiedand those can be updated with multiple realizations which can be usedfor assisted history matching.

As another example, the petrophysical model, and thus a position of theflood front can be generated with deep reading gravity measurements. Forexample, deep reservoir gravity measurements may correspond to the bulkdensity of the formation, ρ_(b). This variable is governed by theEquation 2:ρ_(b)=ρ_(m)(1−ϕ)+ϕ(S _(w)ρ_(w) +S _(o)ρ_(o) +S _(g)ρ_(g)).   Equation 2

Where ρ_(b)=bulk density (from gravity meter data), ρ_(m)=matrix density(from minerology), ρ_(w)=water density (computed from the salinity ofthe water at reservoir conditions), ρ_(o)=oil density (known PhaseBehavior data at reservoir conditions), ρ_(g)=gas density (known phasebehavior data at reservoir conditions), ϕ=porosity (from well logs),S_(w)=water saturation, S_(o)=oil saturation, and S_(g)=gas saturation.Generally, the sum of the oil, water, and gas saturation equals 1, andthe parameters of oil saturation and gas saturation can be combined intoa single S_(hydrocarbon) with a single average density, ρ_(hydrocarbon).

As yet another example, particular deep reservoir measurements (forexample, crosswell EM) or a combination of deep reservoir measurementsdescribed previously can be used to detect the location and movement ofthe flood-front by computing and plotting or mapping first and or secondderivatives (rates of changes), as shown in the FIG. 10. The change insuch derivatives may be used to detect and map the flood-front location.Unexpected changes in derivatives away from the front may indicate aneed to update one or more reservoir properties and update thepetrophysical model.

Method 150 may continue at step 156, which includes determining, basedon the petrophysical model, a flow of an injectant from the injectionwellbore toward the production wellbore. For example, as describedpreviously, the petrophysical model can include the determination of thefloodfront, or saturation (S_(water)+S_(hydrocarbon)=1). The saturationindicates a floodfront position, which shows where the injectant hasflowed from the injection wellbore 102 toward the production wellbore104.

Step 156 can also include updating the petrophysical model using aBayesian inference (for example, an ensemble Kalman filter) using thedeep reservoir measurements. The difference in front propagationvelocity interpreted from any deep reservoir measurement will becompared to that evaluated from simulation results. The difference canbe used to revise reservoir grid-block petrophysical properties, andthus, the petrophysical model generated in step 154. For example, aninitial geological model can be prepared based on, for instance, seismichorizons, well logs, and core data. Petrophysical properties (forexample, porosity and permeability) between the wellbores 102 and 104can be distributed based on Gaussian simulation with multiplerealizations. Representative relative permeability curves obtained fromsteady/unsteady state corefloods are used to simulate multiphase flowduring flooding. After processing the deep reservoir data, a snapshot ofa saturation map at a specific time can be developed. A floodingsimulation can be run up to the time at which deep reading measurementsis acquired. Saturation map obtained from the simulation can be comparedwith the one calculated from deep reading measurements. If there is notan agreement, the permeability field will be modified until a reasonablehistory match is obtained (for example, with a history matching tool,such as, CMG CMG-CMOST from Computer Modeling Group of Calgary, Alberta,Canada, or any other comparable optimization tool). As the injectantfront moves during flooding, each successive deep reading measurementcan provide more information about the distribution of petrophysicalproperties (for example, porosity and permeability). The simulationmodel can therefore be updated after each deep reservoir measurement.

Method 150 may continue at step 158, which includes adjusting an inflowcontrol device (ICD) positioned about the production wellbore based onthe determined flow of the injectant. For example, certain ICDs 106 maybe closed to prevent the floodfront from reaching the productionwellbore 104 at certain intervals (for example, perforation zones of thewellbore 104). Further, certain ICDs 106 may be opened to allowhydrocarbons pushed by the floodfront to reach the production wellbore104 in certain intervals (for example, perforation zones of the wellbore104).

One or more optimization algorithms can be run on the petrophysicalmodel to optimize one or more control settings for the ICDs 106. Forexample, multiple simulations can be conducted using a calibrated (forexample, history-matched) model and compared to select the ICD controlsettings that would result in an optimum recovery. In some instances, anoptimum recovery may include mitigating an early injectant breakthroughinto the production wellbore 104. In some instances, an optimum recoverymay include directing, through control of the ICDs 106, a hydrocarbonflow through particular intervals in the wellbore 104 so as to push thefloodfront away from such intervals. ICDs, in some aspects, may beeither open or closed, while ICVs, in some aspects, may be controllablymodulated between 0% open and 100% open. When deep reservoirmeasurements are not in real time, adjustment of the ICV/ICDs 106 maydepend on how frequently these measurements are conducted.

Method 150 may be iteratively executed (for example, looped back to step152 after step 158). For example, additional deep reservoir measurementscan be taken or identified in step 152 after adjustment of the ICDs/ICVs106. In some instances, additional measurements can be taken oridentified after each successive ICD activation and used to evaluate theongoing success of the ICD schedule and further revise the reservoirproperty distribution. Previous deep reservoir measurements (from aprevious iteration or iterations) may be compared to the most recent, orcurrent, deep reservoir measurements to calibrate the simulation modeland change the settings of the ICDs/ICVs 106. The resulting change inmeasurements, subsequent to settings changes, can provide feedback of asystemic reservoir hydraulic response to the ICD changes.

The iterative process may be ended, for example, once a determinationhas been made that a robust version of the petrophysical model has beenachieved that doesn't change with time. As another example, theiterative process may stop when it is determined that the deep reservoirmeasurements do not change with a change in ICD/ICV control settings. Asanother example, the iterative process may be ended when hydrocarbonrecovery is stopped.

FIG. 2 illustrates several synthetic geomodels with differentheterogeneity in a permeability field in the previously describedsimulation model (FIGS. 1A and 1B) of an injectant detection system andprocess. FIG. 2 shows the different synthetic geomodels that may bebuilt during the simulation process of the injection wellbore 102 andthe production wellbore 104 (shown in FIGS. 1A-1B) to investigate aneffect of heterogeneity in a permeability field on oil recovery, waterproduction, and breakthrough. For example, the more heterogeneous areservoir (rock formation) is, the more variable the rock properties inthat reservoir may be. For example, shale gas reservoirs areheterogeneous formations whose mineralogy, organic content, naturalfractures, and other properties can vary from place to place. Other rockformations may be more homogeneous, where properties do not vary fromlocation to location. As shown in the models 200, a measure ofpermeability (in millidarcys) between the wellbore 102 and 104 can vary.By using these geomodels in the simulation process (which may include upto 50,000 simulations using different geomodels), the heterogeneity ofthe reservoir can be accounted for.

FIGS. 3A and 3B illustrate water saturation maps for heterogeneous andhomogeneous cases that do not include ICDs or ICVs in a simulation modelof an injectant detection system and process. For example, waterdisplaces oil more uniformly in homogeneous reservoirs than it does inheterogeneous reservoirs. The water phase prefers to flow through theleast resistant pathway from the injector 102 to the producer 104.Therefore, a water breakthrough for a heterogeneous case (such as theGeoModel #1 from FIG. 2 that is used in this simulation) occurs muchearlier than that for a homogeneous case. FIG. 3A shows a graph 300 thatshows a water saturation map (in other words, flow of a water injectantfrom the wellbore 102 to the wellbore 104) over a 50,000 day simulationin a heterogeneous model (GeoModel #1). FIG. 3B shows a graph 350 thatshows a water saturation map (in other words, flow of a water injectantfrom the wellbore 102 to the wellbore 104) over a 50,000 day simulationin a homogeneous model. As illustrated, the water saturation front ismuch different, for example, more concentrated at certain productionintervals, for the heterogeneous case as compared to the homogenouscase.

FIGS. 4A and 4B illustrate cumulative oil and water production graphsover time in heterogeneous and homogeneous cases that do not includeICDs or ICVs in a simulation model of an injectant detection system andprocess. Graphs 400 and 450 show cumulative oil production andcumulative water production, respectively, from the production well 104in both the homogeneous case (dotted lines) and heterogeneous case(solid lines) from water saturation models 300 and 350. As shown,heterogeneity in a permeable field leads to lower oil recovery andhigher water production with an early breakthrough of the injectant.This results in lower areal sweep efficiency and 40% lower oil recoveryin the heterogeneous case in this simulation.

FIGS. 5A and 5B illustrate water saturation maps for heterogeneous andhomogeneous cases that include ICDs or ICVs in a simulation model of aconventional injectant detection system and process. For example, in asimulation in which ICD/ICV control is used according to the injectantdetection process described in FIG. 1C, improvement in an areal sweepefficiency can be significant according to the simulation. In thesimulation, when a water front is detected at the production wellbore104, one or more ICV/ICD is controlled (for example, shut in) if theflowing injectant stream exceeds 10% watercut. In other words, in thesimulation water saturation model shown in FIG. 5A, the ICDs/ICVs arecontrolled subsequent to waterflood detection as is conventionally done.Such action in the simulation may show a diversion of the injectant fromhigh permeability to low permeability regions of the reservoir adjacentthe production wellbore 104. An improved sweep efficiency results inhigher oil production and lower water production with delayedbreakthrough of the injectant.

FIG. 5A shows a water saturation model 500 when the ICDs/ICVs arecontrolled as described previously. FIG. 5B shows a water saturationmodel 550 which does not include ICDs/ICVs and, therefore, is the sameas the water saturation model 300. As shown in model 500, controllingthe ICV/ICDs at the production wellbore 104 improves areal sweepefficiency significantly, helping divert water from high permeability tolow permeability regions.

FIGS. 6A and 6B illustrate cumulative oil and water production graphsover time in cases that include ICDs or ICVs with early detection ofinjectant flood front in a simulation model of an injectant detectionsystem and process according to, for example, method 150 and the presentdisclosure. For example, FIG. 5A shows an ICD/ICV control scheme inwhich such devices are controlled (for example, shut in) after water isdetected after its breakthrough. FIGS. 6A and 6B, however, show graphs600 and 650 of cumulative oil production and cumulative waterproduction, respectively, from a simulation in which deep reservoirmeasurements are used (according to method 150) for pre-breakthroughdetection of a water front. In this simulation, L_(d) is used as adimensionless distance between the injector 102 and the producer 104,which ranges from 0 at the producer 104 to 1 at the injector 102. Thus,if L_(d)=0, then the water front is detected at the production well. IfL_(d)=⅓, then it is detected at a distance of one-third of the wellspacing from the producer 104. As shown in the graph 600 of FIG. 6A,cumulative oil production increases based on control (for example,closing) of the ICDs/ICVs based on earlier water front detection (here,L_(d) of ⅓ vs. L_(d) of 0 or no ICD/ICV control). However, as shown inFIG. 6B, increased oil production may also be accompanied by a slightincrease in water production as shown in graph 650. This may result fromheterogeneity in the permeability field.

FIG. 7 illustrates effects of operation of ICDs or ICVs on a productionwellbore due to early detection of an injectant flood front in asimulation model of an injectant detection system and process. Forexample, based on the location of the floodfront, water front detectionmay not occur adjacent or close to (for example, at a chosen distance,L_(d), from the wellbore 104) some ICDs/ICVs at certain intervals of theproduction wellbore 104 may not be closed as the water front movestoward the wellbore 104. Further, the closing of ICDs/ICVs at otherintervals may direct the water front from high to low permeabilityregions of the reservoir adjacent the wellbore 104. The graph 700 shownin FIG. 7 illustrates this concept, showing that at some ICV/ICDs (forexample, near the circle on the line representing the producer 104),water production is continued even after its breakthrough, since nowater front is detected at a distance from the production well 104 dueto diversion. As a result, those ICV/ICDs are not triggered to shut in.Therefore, not only changes in saturation at a distance away from theproduction well 104 but also changes in watercut at each ICV/ICDs shouldbe considered to trigger ICVs/ICDs.

FIG. 8 illustrates optimum locations for early injectant-front detectionfor several different geomodels in a simulation model of an injectantdetection system and process. For example, due to the problems discussedwith reference to FIG. 7 (non-detection of a water front leading towater production through uncontrolled ICDs/ICVs), it may be preferableto determine an optimum location for early water-front detection,(L_(d))_(opt), between the injector 102 and producer 104 to maximize oilproduction with ICDs/ICVs, while also minimizing water production.

The graph 800 illustrates simulations run with the five differentsynthetic geomodels (shown in FIG. 2) to show the effect of differentheterogeneity in permeability field on the optimum location. Based onthe simulations, in some aspects, (L_(d))_(opt) ranges from 0.25 to 0.35independent of the geomodels used in the simulations. Thus, detectingthe water front (for example, taking deep reservoir measurements) about⅔ of the distance from the injector 102 (and ⅓ of the distance to theproducer 104) led to the best results in cumulative oil productionaccording to the simulations.

According to the simulations executed and shown in the figures, theinjectant detection system and process, when utilized to controlICDs/ICVs on a producer, can yield higher cumulative oil production. Insome aspects, benefits of proactively controlling ICDs/ICVs based onearly front detection can improve sweep efficiency and reduce waterproduction in horizontal waterfloods. Further, early detection of awater front with deep reservoir measurements to control ICDs/ICVsprovides incremental oil recovery. In some aspects, an optimum locationfor early water-front detection exists between an injector and aproducer to improve oil production at the specified injection andproduction constraints. Also, deep reservoir measurements may alsoprovide valuable information about mobility field, which can be used toreduce uncertainty in geological models for better history matching andproduction forecasting.

FIG. 9 is a schematic diagram of a computer system 900. The system 900can be used to carry out the operations described in association withany of the computer-implemented methods described previously, accordingto some implementations, such as implementations of an injectantdetection system and process. In some implementations, computing systemsand devices and the functional operations described in thisspecification can be implemented in digital electronic circuitry, intangibly-embodied computer software or firmware, in computer hardware,including the structures disclosed in this specification (for example,system 900) and their structural equivalents, or in combinations of oneor more of them. The system 900 is intended to include various forms ofdigital computers, such as laptops, desktops, workstations, personaldigital assistants, servers, blade servers, mainframes, and otherappropriate computers, including vehicles installed on base units or podunits of modular vehicles. The system 900 can also include mobiledevices, such as personal digital assistants, cellular telephones,smartphones, and other similar computing devices. Additionally thesystem can include portable storage media, such as, Universal Serial Bus(USB) flash drives. For example, the USB flash drives may storeoperating systems and other applications. The USB flash drives caninclude input/output components, such as a wireless transmitter or USBconnector that may be inserted into a USB port of another computingdevice.

The system 900 includes a processor 910, a memory 920, a storage device930, and an input/output device 940. Each of the components 910, 920,930, and 940 are interconnected using a system bus 950. The processor910 is capable of processing instructions for execution within thesystem 900. The processor may be designed using any of a number ofarchitectures. For example, the processor 910 may be a CISC (ComplexInstruction Set Computers) processor, a RISC (Reduced Instruction SetComputer) processor, or a MISC (Minimal Instruction Set Computer)processor.

In one implementation, the processor 910 is a single-threaded processor.In another implementation, the processor 910 is a multi-threadedprocessor. The processor 910 is capable of processing instructionsstored in the memory 920 or on the storage device 930 to displaygraphical information for a user interface on the input/output device940.

The memory 920 stores information within the system 900. In oneimplementation, the memory 920 is a computer-readable medium. In oneimplementation, the memory 920 is a volatile memory unit. In anotherimplementation, the memory 920 is a non-volatile memory unit.

The storage device 930 is capable of providing mass storage for thesystem 900. In one implementation, the storage device 930 is acomputer-readable medium. In various different implementations, thestorage device 930 may be a floppy disk device, a hard disk device, anoptical disk device, or a tape device.

The input/output device 940 provides input/output operations for thesystem 400. In one implementation, the input/output device 940 includesa keyboard and/or pointing device. In another implementation, theinput/output device 940 includes a display unit for displaying graphicaluser interfaces.

The features described can be implemented in digital electroniccircuitry, or in computer hardware, firmware, software, or incombinations of them. The apparatus can be implemented in a computerprogram product tangibly embodied in an information carrier, forexample, in a machine-readable storage device for execution by aprogrammable processor; and method steps can be performed by aprogrammable processor executing a program of instructions to performfunctions of the described implementations by operating on input dataand generating output. The described features can be implementedadvantageously in one or more computer programs that are executable on aprogrammable system including at least one programmable processorcoupled to receive data and instructions from, and to transmit data andinstructions to, a data storage system, at least one input device, andat least one output device. A computer program is a set of instructionsthat can be used, directly or indirectly, in a computer to perform acertain activity or bring about a certain result. A computer program canbe written in any form of programming language, including compiled orinterpreted languages, and it can be deployed in any form, including asa stand-alone program or as a module, component, subroutine, or otherunit suitable for use in a computing environment.

Suitable processors for the execution of a program of instructionsinclude, by way of example, both general and special purposemicroprocessors, and the sole processor or one of multiple processors ofany kind of computer. Generally, a processor will receive instructionsand data from a read-only memory or a random access memory or both. Theessential elements of a computer are a processor for executinginstructions and one or more memories for storing instructions and data.Generally, a computer will also include, or be operatively coupled tocommunicate with, one or more mass storage devices for storing datafiles; such devices include magnetic disks, such as internal hard disksand removable disks; magneto-optical disks; and optical disks. Storagedevices suitable for tangibly embodying computer program instructionsand data include all forms of non-volatile memory, including by way ofexample semiconductor memory devices, such as EPROM, EEPROM, and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,ASICs (application-specific integrated circuits).

To provide for interaction with a user, the features can be implementedon a computer having a display device such as a CRT (cathode ray tube)or LCD (liquid crystal display) monitor for displaying information tothe user and a keyboard and a pointing device such as a mouse or atrackball by which the user can provide input to the computer.Additionally, such activities can be implemented via touchscreenflat-panel displays and other appropriate mechanisms.

The features can be implemented in a computer system that includes aback-end component, such as a data server, or that includes a middlewarecomponent, such as an application server or an Internet server, or thatincludes a front-end component, such as a client computer having agraphical user interface or an Internet browser, or any combination ofthem. The components of the system can be connected by any form ormedium of digital data communication such as a communication network.Examples of communication networks include a local area network (“LAN”),a wide area network (“WAN”), peer-to-peer networks (having ad-hoc orstatic members), grid computing infrastructures, and the Internet.

The computer system can include clients and servers. A client and serverare generally remote from each other and typically interact through anetwork, such as the described one. The relationship of client andserver arises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular implementations of particularinventions. Certain features that are described in this specification inthe context of separate implementations can also be implemented incombination in a single implementation. Conversely, various featuresthat are described in the context of a single implementation can also beimplemented in multiple implementations separately or in any suitablesubcombination. Moreover, although features may be described previouslyas acting in certain combinations and even initially claimed as such,one or more features from a claimed combination can in some cases beexcised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the implementations described previously should not beunderstood as requiring such separation in all implementations, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular implementations of the subject matter have beendescribed. Other implementations are within the scope of the followingclaims. In some cases, the actions recited in the claims can beperformed in a different order and still achieve desirable results. Inaddition, the processes depicted in the accompanying figures do notnecessarily require the particular order shown, or sequential order, toachieve desirable results. In certain implementations, multitasking andparallel processing may be advantageous.

Thus, particular implementations of the present disclosure have beendescribed. Other implementation s are within the scope of the followingclaims. For example, the actions recited in the claims can be performedin a different order and still achieve desirable results.

What is claimed is:
 1. A computer-implemented method for controllinghydrocarbon production, comprising: (i) identifying a plurality ofreservoir measurements of a subterranean hydrocarbon reservoir locatedbetween at least one injection wellbore and at least one productionwellbore; (ii) processing the identified plurality of reservoirmeasurements to generate a petrophysical model of the subterraneanhydrocarbon reservoir, where processing the identified plurality ofreservoir measurements comprises inverting the reservoir measurements todetermine the petrophysical model; (iii) determining, based on thepetrophysical model, a flow of an injectant from the injection wellboretoward the production wellbore; and (iv) adjusting an inflow controldevice (ICD) positioned about the production wellbore based on thedetermined flow of the injectant, wherein inverting the reservoirmeasurements to determine the petrophysical model comprises obtaining aprobable distribution of resistivity in the subterranean hydrocarbonreservoir that is compatible with the plurality of reservoirmeasurements.
 2. The computer-implemented method of claim 1, furthercomprising receiving the plurality of reservoir measurements from one ormore sensors positioned at at least one of: a terranean surface; or inthe reservoir between the injection wellbore and the productionwellbore.
 3. The computer-implemented method of claim 2, wherein thereservoir measurements comprise at least one of crosswellelectromagnetic (EM), borehole EM, surface electromagnetics, gravitymeasurements, or 4D seismic.
 4. The computer-implemented method of claim3, wherein the petrophysical model comprises a water saturation value ata plurality of locations in the reservoir between the injection wellboreand the production wellbore.
 5. The computer-implemented method of claim4, wherein determining the injectant flow comprises determining afloodfront between the injection wellbore and the production wellbore,the floodfront comprising a sum of the water saturation and ahydrocarbon saturation value at the plurality of locations.
 6. Thecomputer-implemented method of claim 5, wherein determining theinjectant flow comprises updating the petrophysical model using aBayesian inference with the plurality of reservoir measurements.
 7. Thecomputer-implemented method of claim 6, further comprising: determininga threshold location between the injection wellbore and the productionwellbore; and determining the flow of the injectant at the thresholdlocation.
 8. The computer-implemented method of claim 7, whereinadjusting the ICD comprises at least one of: adjusting the ICD based onthe flow of the injectant at the threshold location exceeding apredetermined value; or shutting the ICD.
 9. The computer-implementedmethod of claim 8, further comprising executing an iterative process ofsteps (i) through (iv), the iterative process comprising comparing aprevious plurality of reservoir measurements with a current plurality ofreservoir measurements.
 10. The computer-implemented method of claim 9,further comprising stopping the iterative process when a differencebetween the current plurality of reservoir measurements and the previousplurality of reservoir measurements is less than a threshold value. 11.The computer-implemented method of claim 1, wherein the reservoirmeasurements comprise at least one of crosswell electromagnetic (EM),borehole EM, surface electromagnetics, gravity measurements, or 4Dseismic.
 12. The computer-implemented method of claim 1, wherein thepetrophysical model comprises a water saturation value at a plurality oflocations in the reservoir between the injection wellbore and theproduction wellbore.
 13. The computer-implemented method of claim 1,wherein inverting the reservoir measurements comprises executing theArchie algorithm to the reservoir measurements.
 14. Thecomputer-implemented method of claim 13, wherein determining theinjectant flow comprises determining a floodfront between the injectionwellbore and the production wellbore, the floodfront comprising a sum ofthe water saturation and a hydrocarbon saturation value at the pluralityof locations.
 15. The computer-implemented method of claim 1, whereindetermining the injectant flow comprises updating the petrophysicalmodel using a Bayesian inference with the plurality of reservoirmeasurements.
 16. The computer-implemented method of claim 1, furthercomprising: determining a threshold location between the injectionwellbore and the production wellbore; and determining the flow of theinjectant at the threshold location.
 17. The computer-implemented methodof claim 16, wherein adjusting the ICD comprises at least one of:adjusting the ICD based on the flow of the injectant at the thresholdlocation exceeding a predetermined value; or shutting the ICD.
 18. Thecomputer-implemented method of claim 1, further comprising executing aniterative process of steps (i) through (iv), the iterative processcomprising comparing a previous plurality of reservoir measurements witha current plurality of reservoir measurements.
 19. Thecomputer-implemented method of claim 18, further comprising stopping theiterative process when a difference between the current plurality ofreservoir measurements and the previous plurality of reservoirmeasurements is less than a threshold value.
 20. Thecomputer-implemented method of claim 1, wherein the inversion comprisesan inference of the probable distribution of resistivity in thesubterranean hydrocarbon reservoir.